期刊
IEEE ACCESS
卷 8, 期 -, 页码 174530-174541出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3025589
关键词
Time series analysis; Neural networks; Predictive models; Prediction algorithms; Decision trees; Particle swarm optimization; Machine learning algorithms; Available parking space; gradient boosting decision tree; particle swarm optimization; short-term prediction; wavelet neural network
资金
- Natural Science Foundation of Zhejiang Province, China [LY20E080011]
- National Natural Science Foundation of China [71971059, 71701108, 71861006]
- National Key Research and Development Program of China-Traffic Modeling, Surveillance and Control with Connected and Automated Vehicles [2017YFE9134700]
- Natural Science Foundation of Jiangsu Province, China [BK20180775]
Reliable short-term prediction of available parking space (APS) is the basic theory of parking guidance information system (PGIS). Based on the Intelligent parking system at the Eastern New Town, Yinzhou District, Ningbo, China, this study collected the data of parking availability in the on-street parking areas. The variation characteristics of APS were investigated and analyzed at different spatial-temporal levels. Then the APS prediction models based on Gradient Boosting Decision Tree (GBDT) and Wavelet Neural Network (WNN) were proposed. Furthermore, an improved WNN algorithm with (WA) decomposition and Particle Swarm Optimization (PSO) were presented. The original time series was decomposed and reconstructed by wavelet analysis, and the WNN algorithm found the optimal threshold of initial weight through PSO. The result of GBDT (weekday: MSE = 27.37, S-MSE = 0, TIME = 35min, weekend: MSE = 9.9, S-MSE = 0, TIME = 35min) and WA-PSO-WNN (weekday: MSE = 14.93, S-MSE = 1.88, TIME = 160.32s, weekend: MSE = 12.33, S-MSE = 10.23, TIME = 160.95s) approximated the true value. But the prediction time of GBDT was too long to be applicable to the short-term prediction of APS in this paper. Compared with the methods of GBDT, WNN, and PSO-WNN, the WA-PSO-WNN algorithm performs much better. The average differences in MSE between WA-PSO-WNN and GBDT for weekday and weekend data are 45.45% and 58.76%, respectively, indicating that WA-PSO-WNN can increase the prediction accuracy of weekday and weekend data by an average of 45.45% and 58.76% compared with the GBDT model. Finally, the application prospects of short-term APS forecasting were also discussed in reducing cruising parking behavior, reducing illegal parking behavior and adjusting dynamic parking rates to verify the importance of APS short-term forecasting.
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